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Anisotropic Scaling Matrices and Subdivision Schemes Anisotropic Scaling Matrices and Subdivision Schemes Mira Bozzini1, Milvia Rossini2, Tomas Sauer3 and Elena Volontè4 1 Università degli studi Milano-Bicocca, Italy, [email protected] 2 Università degli studi Milano-Bicocca, Italy, [email protected] 3Universität Passau, Germany , [email protected] 4 Università degli studi Milano-Bicocca, Italy, [email protected] Abstract Unlike wavelet, shearlets have the capability to detect directional discontinuities together with their directions. To achieve this, the considered scaling matrices have to be not only expanding, but also anisotropic. Shearlets allow for the definition of a directional multiple multiresolution analysis, where perfect reconstruction of the filterbank can be easily ensured by choosing an appropriate interpolatory multi- ple subdivision scheme. A drawback of shearlets is their relative large determinant that leads to a substantial complexity. The aim of the paper is to find scaling matrices in Zd×d which share the properties of shearlet matrices, i.e. anisotropic expanding matrices with the so- called slope resolution property, but with a smaller determinant. The proposed matrices provide a directional multiple multiresolution anal- ysis whose behaviour is illustrated by some numerical tests on images. 1 Introduction Shearlets were introduced by [6] as a continuous transformation and quite immediately became a popular tool in signal processing (see e.g. [7]) due to the possibility of detecting directional features in the analysis process. In the shearlet context, the scaling is performed by matrices of the form 4Ip 0 d×d M = D2S; D2 = 2 R ; 0 < p < d; (1) 0 2Id−p 1 and I ∗ S = p : (2) 0 Id−p The diagonal matrix D2 results in the the anisotropy of the resulting shear- lets while the so-called shear matrix S, is responsible for the directionality of the shearlets. With a proper mix of these two properties, shearlets give a wavelet-like construction that is suited to deal with anisotropic problems such as detection of the singularities along curves or wavefront resolution [7, 8, 3]. In contrast to other approaches, such as curvelets or ridgelets, shearlets admit, besides a transform, also a suitably extended version of the multires- olution analysis and therefore an efficient implementation in terms of filter- banks. This is the concept of Multiple Multiresolution Analysis (MMRA) [8]. As for the classical multiresolution analysis, also the MMRA can be con- nected to a convergent subdivision scheme, or more precisely to a multiple subdivision scheme [13]. The refinement in a multiple subdivision scheme is controlled by choosing different scaling matrices from a finite dictionary (Mj : j 2 Zs), where we use the convenient abbreviation Zs := f0; : : : ; s−1g. If those scaling matrices are products of a common anisotropic dilation ma- trix with different shears, the resulting shearlet matrices yield a directionally adapted analysis of a signal. In particular, if we set Mj = D2Sj, j 2 Zs, an n–fold product of such matrices, n M := Mn ··· M1 ; 2 Zs ; allows to explore singularities along a direction directly connected to the sequence . In the shearlet case, this connection is essentially a binary expansion of the slope [8] while for more general matrices the relationship can be more complicated [2]. The key ingredient for the construction of a multiple subdivision scheme and its associated filterbanks [12] is the dictionary of scaling matrices. It turns out that, in order to have a well–defined (interpolatory) multiple sub- division scheme and perfect reconstruction filterbanks, we need to consider matrices (Mj : j 2 Zs) that are expanding individually and jointly expanding considering them all together. Shearlet scaling matrices quite automatically satisfy these properties and they are also able to catch all the directions in the space due to the so-called slope resolution property. This means that any possible directions can be generated by M applied to a given refer- ence direction. Therefore, any transform that analyses singularities across this reference direction in the unsheared case, analyses singularities across arbitrary lines for an appropriate . 2 The drawback of the discrete shearlets is the huge determinant of the scaling matrix which is 2p+d, as minimum, and increases dramatically with the dimension d. The determinant of the scaling matrix gives the number of analysis and synthesis filters needed in the critically sampled filterbank, soit is related to the efficiency and the computational cost of any implementation. Therefore, it is worthwhile to try to reduce, or even better minimize it. The aim of this paper is to present a family of matrices with lower determi- nant that in any dimension d enjoys all the valuable properties of shearlets by essentially just giving up the requirement that the scaling has to be parabolic. This was also the aim of [2], where the authors present a set of matrices in 2×2 Z with minimum determinant among anisotropic integer valued matrices. d×d In contrast to that, here we consider matrices M 2 Z which are the product of an anisotropic diagonal matrix of the form aI 0 D = p ; a 6= b > 1; 0 < p < d; (3) 0 bId−p with a shear matrix. The choice to work with matrices of this form is motivated by the desire to preserve another feature of the shearlet matrices: the so-called pseudo-commuting property [1]. Even if this property is not necessary to construct a directional multiresolution analysis, it is very useful n to give an explicit expression for the iterated matrices M, 2 Zs and the −1 d associated refined grid M Z . In this setting, the matrices with minimal determinant are 3I 0 D = p : (4) 0 2Id−p In this paper, we consider exactly this type of matrices and we prove that this new family has all the desired properties: they are expanding, jointly expanding, and provide the slope resolution property as well as the pseudo commuting property. Furthermore, it is possible to generate convergent multiple subdivision schemes and filterbanks related to these matrices. The paper is organized as follows. In Section 2, we recall some background material. Sections 2.2 and 2.3 introduce the concepts of MMRA and the related construction of multiple subdivision schemes and filterbanks for a general set of jointly expanding matrices. In Sections 2.4 we study the properties of shearlet scaling matrices and in Section 2.5 we recall some theorems by [1] that show which unimodular matrices pseudo commute with an anisotropic diagonal matrix in dimension d. This allows us to find pseudo- commuting matrices. Then, in Section 3, we introduce our choice of scaling 3 matrices for a general dimension d. These matrices are pseudo commuting matrices and we prove that they satisfy all the requested properties. Finally, in Section 4 few examples illustrate how our bidimensional matrices work on images. 2 Background material In this section, we recall some of the fundamental concepts needed for MM- RAs. 2.1 Basic notation and definitions All concepts that follow are based on expanding matrices which are the key ingredient and fix the way to refine the given data, hence to manipulate d×d d them. A matrix M 2 Z is called a dilation or expanding matrix for Z if all its eigenvalues are greater than one in absolute value, or, equivalently if kM −nk ! 0 for some matrix norm as n increases. These properties suggest that j det Mj is an integer ≥ 2. A matrix is said to be isotropic if all its eigenvalues have the same modulus and is called anisotropic if this is not the case. In this paper we consider a set of s expanding matrices, denoted by (Mj : j 2 Zs) where Zs := f0; : : : ; s − 1g. d d In order to recover the lattice Z from the shifts of the sublattice MZ , we have to consider the cosets d d d d d Zξ := ξ + MZ := fξ + Mk : k 2 Z g; ξ 2 M[0; 1) \ Z ; where ξ is called the representer of the coset. Any two cosets are either d identical or disjoint, and the union of all cosets gives Z ; the number of cosets is j det Mj. For the definition of a subdivision scheme, we have to fix some notation for infinite sequences. The space of real valued bi-infinite sequences indexedby d d d Z or, equivalently, the space of all functions Z ! R is denoted by `(Z ). d By `p(Z ), 1 ≤ p < 1, we denote the sequences such that 0 11=p X p kck d := jc(α)j < 1; `p(Z ) @ A α2Zd d d and use `1(Z ) for all bounded sequences. Finally, `00(Z ) is set of sequences d from Z to R with finite support. 4 2.2 Multiple subdivision schemes and MMRA The concept of multiresolution analysis provides the theoretical background needed in the discrete wavelets context. Unlike curvelets and ridgelets, shearlets allow to define and use a fully discrete transform based oncas- caded filterbanks using the concept of MMRA. Here we recall the basic as- pects of MMRA and show when and how it is possible to generate a multiple multiresolution analysis with different families of scaling matrices. d d d A subdivision operator S : `(Z ) ! `(Z ) based on a mask a 2 `00(Z ) maps a sequence c 2 `(Zd) to the sequence given by X Sc = a(· − Mα) c(α); α2Zd −1 d which is then interpreted as a function on the finer grid M Z . An algebraic tool in studying subdivision operators is its symbol, defined as the Laurent polynomial whose coefficients are the values of the mask a, ∗ X α d a (z) = a(α)z ; z 2 (C n f0g) : α2Zd As shown by [13], it is always possible to generate MMRA for an arbitrary family of jointly expanding scaling matrices (Mj : j 2 Zs) in the shearlet context for an arbitrary dimension d, based on a generalization of subdivision schemes: multiple subdivision.
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